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Background <p>Matrix metalloproteinase-2 (MMP-2) secretion homeostasis, governed by the multifaceted interplay of skin stretching, is a pivotal determinant influencing wound healing dynamics. This investigation endeavors to devise an artificial intelligence (AI) prediction framework delineating the modulation of MMP-2 expression under stretching conditions, thereby unravelling profound insights into the mechanobiological orchestration of MMP-2 secretion and fostering novel mechanotherapeutic strategies targeted at MMP-2 modulation.</p> Methods <p>Employing a bespoke mechanical tensile loading apparatus, diverse mechanical tensile stimuli were administered to fibroblasts, with parameters such as tensile shape and frequency duration constituting the mechanical loading regimen. Furthermore, reverse transcription polymerase chain reaction (RT‒PCR) assays were conducted to measure MMP-2 gene expression levels in fibroblasts subjected to mechanical stretching. Subsequently, the resulting data were partitioned into training and validation cohorts at a 7:3 ratio, facilitating the development of the deep learning (DL) model via a back propagation neural network predicated on the training set. An external validation set was also curated by culling pertinent literature from the PubMed database to assess the predictive ability of the model.</p> Results <p>Analysis of 336 data points related to MMP-2 gene expression via RT‒PCR corroborated the variability in MMP-2 gene expression levels in response to distinct mechanical stretching regimens. Consequently, a DL model was successfully crafted via the backpropagation algorithm to delineate the impact of mechanical stretching stimuli on MMP-2 gene expression levels. The model, characterized by an <i>R</i><sup>2</sup> value of 0.73, evinced a commendable fit with the training data set, elucidating the intricate interplay between the input features and the target variable. Notably, the model exhibited minimal prediction errors, as evidenced by a root mean square error (RMSE) of 0.42 and a mean absolute error (MAE) of 0.28. Furthermore, the model showcased robust generalization capabilities during validation, yielding <i>R</i><sup>2</sup> values of 0.70 and 0.71 for the validation and external validation sets, respectively, revealing its predictive accuracy.</p> Conclusions <p>The DL model fashioned through the backpropagation algorithm adeptly forecasts the impact of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts with relative precision. These findings provide a foundation for the modulation of MMP homeostasis via mechanical stretching to expedite the healing of recalcitrant chronic refractory wound (CRW).</p>

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Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月5日

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  • Ruozu Xiao,
  • Haowei Zhou,
  • Zhen Shi,
  • Rong Huang,
  • Yuheng Zhang,
  • Jing Li

摘要

Background

Matrix metalloproteinase-2 (MMP-2) secretion homeostasis, governed by the multifaceted interplay of skin stretching, is a pivotal determinant influencing wound healing dynamics. This investigation endeavors to devise an artificial intelligence (AI) prediction framework delineating the modulation of MMP-2 expression under stretching conditions, thereby unravelling profound insights into the mechanobiological orchestration of MMP-2 secretion and fostering novel mechanotherapeutic strategies targeted at MMP-2 modulation.

Methods

Employing a bespoke mechanical tensile loading apparatus, diverse mechanical tensile stimuli were administered to fibroblasts, with parameters such as tensile shape and frequency duration constituting the mechanical loading regimen. Furthermore, reverse transcription polymerase chain reaction (RT‒PCR) assays were conducted to measure MMP-2 gene expression levels in fibroblasts subjected to mechanical stretching. Subsequently, the resulting data were partitioned into training and validation cohorts at a 7:3 ratio, facilitating the development of the deep learning (DL) model via a back propagation neural network predicated on the training set. An external validation set was also curated by culling pertinent literature from the PubMed database to assess the predictive ability of the model.

Results

Analysis of 336 data points related to MMP-2 gene expression via RT‒PCR corroborated the variability in MMP-2 gene expression levels in response to distinct mechanical stretching regimens. Consequently, a DL model was successfully crafted via the backpropagation algorithm to delineate the impact of mechanical stretching stimuli on MMP-2 gene expression levels. The model, characterized by an R2 value of 0.73, evinced a commendable fit with the training data set, elucidating the intricate interplay between the input features and the target variable. Notably, the model exhibited minimal prediction errors, as evidenced by a root mean square error (RMSE) of 0.42 and a mean absolute error (MAE) of 0.28. Furthermore, the model showcased robust generalization capabilities during validation, yielding R2 values of 0.70 and 0.71 for the validation and external validation sets, respectively, revealing its predictive accuracy.

Conclusions

The DL model fashioned through the backpropagation algorithm adeptly forecasts the impact of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts with relative precision. These findings provide a foundation for the modulation of MMP homeostasis via mechanical stretching to expedite the healing of recalcitrant chronic refractory wound (CRW).